Obtaining TFR From Incomplete and Phase-Corrupted m-D Signal in Real Time

Wang, Huan; Zhang, Qun; Luo, Ying; Kang, Le; Lu, Xihua

doi:10.1109/lgrs.2020.3033809

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…The Fourier transform is unsuitable for analyzing the nonstationary signal. Time-frequency analysis is an effective method for representing the energy of a nonstationary signal in the time-frequency plane in which the nonstationary signal can reveal features that cannot be found in the frequency domain [20]. The short-time Fourier transform (STFT) is used to analyze the micro-Doppler of the radar returns.…”

Section: Signal Model and Problem Formulationmentioning

confidence: 99%

TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net

et al. 2022

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The micro-motion target echoes can be regarded as the accumulation of a few strong scattering points echoes, which are naturally sparse. Therefore, the compressed sensing (CS) reconstruction method can be used to analyze the incomplete micro-Doppler signal and extract the micro-motion features. Traditional CS reconstruction algorithms are time-consuming and sensitive to the selection of model parameters, limiting the performance of micro-motion feature extraction. This paper proposes a deep unfolded method to achieve the reconstruction of the time-frequency representation (TFR) of incomplete micro-Doppler signals. First, the joint time-frequency (JTF) transform is used to construct the CS model and the Training data is obtained according to the model. Then, the alternating direction method of multipliers (ADMM) algorithm is constructed into an iterative network named ADMM-net corresponding to the iterative solution process. An auxiliary loss is designed into the ADMM-net called AL-ADMM-net to improve the quality of reconstruction. The AL-ADMM-net is trained by the training data to learn the optimal parameters. Furthermore, an AL-ADMM-net iterative method is proposed when the micro-Doppler signal has phase corruption. Simulations are given to prove the effectiveness of the proposed method.

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Section: Signal Model and Problem Formulationmentioning

confidence: 99%

TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net

et al. 2022

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“…Bennett et al [26] modeled the aerodynamics of UAV m-D signatures' point amounts. Moreover, several methods and systems for classifying drones and birds of prey identification are described in [27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

A Micro-Motion Parameters Estimation Method for Multi-Rotor Targets without a Prior

Ren,

Liang,

Wang

et al. 2024

Remote Sensing

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Multi-rotor aircraft have the advantages of a simple structure, low cost, and flexible operation in the unmanned aerial vehicle (UAV) family, and have developed rapidly in recent years. Radar surveillance and classification of the growing number of multi-rotor aircraft has become a challenging problem due to their low-slow-small (LSS) characteristics. Estimation of the blade number is an important step in distinguishing LSS targets. However, most of the current research on micro-motion parameters estimation has focused on the analysis of rotational frequency, length, and the initial phase of blades with a prior of blade number, affecting its ability to identify LSS targets. In this article, a micro-motion parameters estimation method for multi-rotor targets without a prior is proposed. On the basis of estimating the flashing frequency of the blades, a validation function is constructed through spectral analysis to judge the number of blades, and then the rotational frequency is estimated. The blade length is calculated by estimating the maximum Doppler shift. Moreover, the variational mode decomposition (VMD)-based atomic scaling orthogonal matching pursuit (AS-OMP) method is jointly applied to estimate the blade length when suffering from the low PRF and insufficient SNR conditions. Extensive experiments on the simulated and measured data demonstrate that the proposed method outperforms robust micro-motion parameter estimation capability in low PRF and insufficient SNR conditions compared to the traditional time-frequency analysis methods.

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Iterative recovery algorithm for Incomplete and Phase-Corrupted micro-Doppler signals in real-time

Wang

et al. 2022

2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE)

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Obtaining TFR From Incomplete and Phase-Corrupted m-D Signal in Real Time

Cited by 4 publications

References 14 publications

TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net

TFR Recovery From Incomplete Micro-Doppler Signal via AL-ADMM-Net

A Micro-Motion Parameters Estimation Method for Multi-Rotor Targets without a Prior

Iterative recovery algorithm for Incomplete and Phase-Corrupted micro-Doppler signals in real-time

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